_os.getenv("TF_USE_MODULAR_FILESYSTEM", "0") == "1"):
  import tensorflow_io_gcs_filesystem as _tensorflow_io_gcs_filesystem

# Lazy-load Keras v2/3.
_tf_uses_legacy_keras = (
    _os.environ.get("TF_USE_LEGACY_KERAS", None) in ("true", "True", "1"))
setattr(_current_module, "keras", _KerasLazyLoader(globals()))
_module_dir = _module_util.get_parent_dir_for_name("keras._tf_keras.keras")
_current_module.__path__ = [_module_dir] + _current_module.__path__

                       var_name,
                       query_item,
                       enabled_by_default,
                       question=None,
                       yes_reply=None,
                       no_reply=None,
                       bazel_config_name=None):
  """Set boolean action_env variable.

  Ask user if query_item will be enabled. Default is used if no input is given.

  }
  return absl::OkStatus();
}

// TODO(agarwal): use an automatic mechanism for handling None arguments to
// gradient functions.
//
// Some gradient functions can accept None arguments for gradients. The
// following maps the operation name to the indices at which the corresponding
// gradient function can accept None values. e.g. FusedBatchNorm outputs 5
// values and hence receives 5 gradient values during backprop. However the

  EXPECT_TRUE(neg == neg2);
  NodeDef node_def2;
  ASSERT_TRUE(GetNodeDef(neg2, &node_def2));
  EXPECT_EQ(node_def.DebugString(), node_def2.DebugString());

  TF_Operation* feed2 = TF_GraphOperationByName(graph, "feed");
  EXPECT_TRUE(feed == feed2);
  ASSERT_TRUE(GetNodeDef(feed, &node_def));
  ASSERT_TRUE(GetNodeDef(feed2, &node_def2));
  EXPECT_EQ(node_def.DebugString(), node_def2.DebugString());

        data, or by relaxing `Input` shape assumptions (note that you can pass
        shapes with `None` entries for axes that are meant to be dynamic). You
        can also disable the input checking entirely by setting
        `model.input_spec = None`.
    *   Several changes have been made to
        `tf.keras.mixed_precision.experimental`. Note that it is now recommended
        to use the non-experimental `tf.keras.mixed_precision` API.

      // added since the last TF_SessionRun() call.
      for (auto id = session->last_num_graph_nodes; id < num_nodes; ++id) {
        Node* const node = graph.FindNodeId(id);
        if (node != nullptr && node->IsOp()) {
          NodeDef* const node_def = graph_def.add_node();
          *node_def = node->def();
        }
      }
      *graph_def.mutable_library() = graph.flib_def().ToProto();

# google internal build on configurable attributes.
#
# For non-configurable distinction between OSS and Google builds, see
# `if_oss()` and `if_google()` macros in tensorflow.bzl.
config_setting(
    name = "oss",
    flag_values = {":oss_setting": "True"},
    visibility = ["//visibility:public"],
)

# Non-configurable setting to indicate open source build.
bool_setting(
    name = "oss_setting",

    --enable_bzlmod=false \
    //:requirements_"$VERSION".update
  sed -i '/^#/d' requirements_lock_"$VERSION".txt
  mv requirements_lock_"$VERSION".txt ../../../requirements_lock_"$VERSION".txt
done

// policy is applied to any subsequent op executions.
TF_CAPI_EXPORT void TFE_ContextSetSoftDevicePlacement(TFE_Context* ctx,
                                                      unsigned char enable,
                                                      TF_Status* status);

// Configure device placement policy logging for the eager executor. Note this

            "//tensorflow/cc:while_loop",
            "//tensorflow/cc/saved_model:loader_lite",
            "//tensorflow/compiler/mlir/tfr:graph_decompose_pass",
            "//tensorflow/compiler/mlir/tfr:node_expansion_pass",
            "//tensorflow/core:core_cpu",
            "//tensorflow/core:core_cpu_internal",
            "//tensorflow/core:framework",
            "//tensorflow/core:lib",